External APIs break in predictable ways. They rate-limit your requests during peak hours. They change field names without warning. They return HTTP 200 with an error object nested three levels deep in JSON. They timeout after exactly 29 seconds, one second before your own timeout fires. If your data connector treats these as exceptional cases, your pipeline stops working the day your business depends on it. I have built data connectors for regulated environments where the external API was a legacy SOAP service, a partner REST endpoint with unpredictable availability, and a SaaS vendor that changed response schemas without versioning. The lesson from all three is the same: resilience is not a retry loop wrapped around an HTTP client. It is a design decision about failure ownership, state management, and operational visibility. Failure Modes You Must Design For, Not Catch Later Most data connector failures fall into three categories: transient errors you can retry, permanent error...
A green build does not mean your deployment worked. I learned this the hard way while modernizing a legacy .NET system that had spent years accumulating manual release steps outside the automation. The CI/CD pipeline would pass, the artifact would land in the environment, and then someone would remember the configuration flag that never made it into the repository, or the database migration script that lived in a wiki, or the API key rotation that happened through a support ticket. The pipeline said success, but the application was broken in production. The problem was not the CI/CD tool. We were using GitHub Actions, which is capable and flexible. The problem was that CI/CD had been treated as a checkbox—something you set up once to automate the build and maybe run a few tests—rather than a system designed to make deployments smaller, safer, and repeatable. The hidden manual steps were not documented anywhere the pipeline could see them, so they became failure points every time we ...